Own the pager

Deploy it and carry it. Put it behind a real endpoint with authentication, rate limiting and a hard spending cap, because an unbounded AI endpoint on the public internet is an invoice waiting to happen. Add tracing across the whole request rather than the model call alone, so retrieval, tool calls and the database land on one timeline. Set alerts on the things that actually hurt: error rate, p99 latency, and spend per hour crossing a threshold you picked in advance rather than after the fact. Write the runbook before you need it, covering the three failures you can already predict, provider outage, latency spike and cost spike, and what a person does in each. Then use it for real. Invite users, even ten of them, and watch. Something will go wrong inside a week and it will not be the thing you prepared for. Write an incident note: what happened, how you noticed, what you did, and what you changed so it cannot happen the same way again. That note will be worth more in an interview than the deployment it describes.

The common failure is deploying without a spending cap, which is fine right up until a loop or a crawler finds you. Put the cap in before the domain name. The other failure is waiting for an incident serious enough to be worth writing up. There is no such thing as too small. A five-minute outage that your own alert caught, that you diagnosed and fixed, demonstrates the entire loop, and the loop is the thing being hired for.